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The purpose of this study is to propose a systematic model selection procedure for long-term load forecasting (LTLF) for ex-ante and ex-post cases considering uncertainty in exogenous predictors.

The different variants of regression models, namely, Polynomial Regression (PR), Generalised Additive Model (GAM), Quantile Polynomial Regression (QPR) and Quantile Spline Regression (QSR), incorporating uncertainty in exogenous predictors like population, Real Gross State Product (RGSP) and Real Per Capita Income (RPCI), temperature and indicators of breakpoints and calendar effects, are considered for LTLF. Initially, the Backward Feature Elimination procedure is used to identify the optimal set of predictors for LTLF. Then, the consistency in model accuracies is evaluated using point and probabilistic forecast error metrics for ex-ante and ex-post cases.

From this study, it is found PR model outperformed in ex-ante condition, while QPR model outperformed in ex-post condition. Further, QPR model performed consistently across validation and testing periods. Overall, QPR model excelled in capturing uncertainty in exogenous predictors, thereby reducing over-forecast error and risk of overinvestment.

These findings can help utilities to align model selection strategies with their risk tolerance.

To propose the systematic model selection procedure in this study, the consistent performance of PR, GAM, QPR and QSR models are evaluated using point forecast accuracy metrics Mean Absolute Percentage Error, Root Mean Squared Error and probabilistic forecast accuracy metric Pinball Score for ex-ante and ex-post cases considering uncertainty in the considered exogenous predictors such as RGSP, RPCI, population and temperature.

The purpose of this paper is to propose a forecasting model to predict the demand under uncertain environment to control the bullwhip effect (BWE) considering review-period order-up-to level ((R, S)) inventory control policy and its different variants such as (R, βS) (R, γO) and (R, γO, βS) proposed by Jakšič and Rusjan, (2008) and Bandyopadhyay and Bhattacharya (2013).

A hybrid forecasting model has been developed by combining the feature of discrete wavelet transformation (DWT) and an intelligence technique, multi-gene genetic programming (MGGP), denoted as DWT-MGGP. Performance of DWT-MGGP model has been verified under (R, S) inventory control policy considering demand from three different manufacturing companies.

A comparison between DWT-MGGP model and autoregressive integrated moving average forecasting model has been done by estimating forecast error and BWE. Further, this study has been extended with analysing the behaviour of BWE considering different variants of (R, S) policy such as (R,βS) (R, γO) and (R,γO,βS) and found that BWE can be moderated by controlling the inventory smoothing (β) and order smoothing parameters (γ).

This study is limited to different variants of (R, S) inventory control policy. However, this study can be further extended to continuous review policy.

The proposed DWT-MGGP model can be used as a suitable demand forecasting model to control the BWE when (R, S), (R,βS) (R,γO) and (R,γO,βS)inventory control policies are followed for replenishment.

This study analyses the behavior of BWE through controlling the inventory smoothing (β) and order smoothing parameters (γ) when demand is predicted using DWT-MGGP forecasting model and order is estimated using (R, S), (R,βS) (R,γO) and (R,γO,βS) inventory control policies.

The purpose of this paper is to provide a simulation modelling framework to examine the behaviour of a serial make-to-stock (MTS) manufacturing system under the influence of various uncertainties. Further, the study analyses effect of propagation uncertainties from lower to upper stream of supply chain.

System dynamics modelling approach has been adopted for modelling and analysing the behaviour of a serial MTS manufacturing system under the influence of different uncertainties such as demand, supplier acquisition rate, raw material (RM) supply lead time, processing time and delay due to machine failure. The backup supply strategy has been proposed to mitigate the adverse effect of the RM supply uncertainty.

The effect of variations of various factors on the performance of a MTS manufacturing supply chain in measured through various performance measures like work-in-progress (WIP) inventory, backlog and RM shortage at both manufacturer’s and supplier’s end. The benefit of adopting backup supply strategy under RM supply uncertainty is demonstrated.

This work is limited to analysis of a serial MTS manufacturing system dealing with a single product having two machines only. The study can be easily extended to a more complex system with multiple machines, lines and products.

A simple simulation framework has been proposed to analyse the effect of various uncertainties on the performance of a MTS manufacturing system. The managers can simulate complex systems using simulation approaches to generate if-then scenarios to gain insight into practical problems and formulate strategies to mitigate adverse effect of uncertainties at various level of supply chain.

The study analyses behaviour of MTS manufacturing system under the effect of various uncertainties operating simultaneously in the system. A backup supplier strategy is proposed to improve the service level at the customer’s end through improving service level at the supplier’s end. Similarly, effective strategies can be tested with the proposed simple model to reduce the effect of uncertainty at different levels of the supply chain.